{"title":"基于支持向量机方法的HIV-1蛋白酶构效关系研究","authors":"Yang Li, Xiaomeng Li, Ping Ma","doi":"10.1109/icccs55155.2022.9846750","DOIUrl":null,"url":null,"abstract":"Human immunodeficiency virus type 1 (HIV-1) is a kind of retrovirus and can cause AIDS (Acquired Immune Deficiency Syndrome). HIV-1 protease is a retroviral aspartyl protease which is essential for the life-cycle of HIV. HIV-1 protease inhibitors can decrease the activity of HIV-1, and this can reduce the infections of AIDS. In the study of HIV-1protease structure-activity relationship, we built a data set of 1630 compounds including 830 protease inhibitors and 800 HIV-1 protease decoys. Support Vector Machine (SVM) was used to build the classification model for HIV-1 protease inhibitors and decoys and the quantitative prediction model for HIV-1 protease inhibitors. In the study we use ADRIANA.Code software to calculate the descriptors of HIV-1 protease inhibitors and HIV-1 protease decoys, including 2D and 3D descriptors. The classification model and quantitative prediction model were built for HIV-1 protease inhibitors. The accuracy rates of classification models are over 98%, and Matthews Correlation Coefficient (MCC) of classification models are over 0.96. The linear regression coefficients of the quantitative prediction models are above 0.75","PeriodicalId":121713,"journal":{"name":"2022 7th International Conference on Computer and Communication Systems (ICCCS)","volume":"239 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Research on Structure-Activity Relationship of HIV–1 Protease Based on Support Vector Machine Method\",\"authors\":\"Yang Li, Xiaomeng Li, Ping Ma\",\"doi\":\"10.1109/icccs55155.2022.9846750\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Human immunodeficiency virus type 1 (HIV-1) is a kind of retrovirus and can cause AIDS (Acquired Immune Deficiency Syndrome). HIV-1 protease is a retroviral aspartyl protease which is essential for the life-cycle of HIV. HIV-1 protease inhibitors can decrease the activity of HIV-1, and this can reduce the infections of AIDS. In the study of HIV-1protease structure-activity relationship, we built a data set of 1630 compounds including 830 protease inhibitors and 800 HIV-1 protease decoys. Support Vector Machine (SVM) was used to build the classification model for HIV-1 protease inhibitors and decoys and the quantitative prediction model for HIV-1 protease inhibitors. In the study we use ADRIANA.Code software to calculate the descriptors of HIV-1 protease inhibitors and HIV-1 protease decoys, including 2D and 3D descriptors. The classification model and quantitative prediction model were built for HIV-1 protease inhibitors. The accuracy rates of classification models are over 98%, and Matthews Correlation Coefficient (MCC) of classification models are over 0.96. The linear regression coefficients of the quantitative prediction models are above 0.75\",\"PeriodicalId\":121713,\"journal\":{\"name\":\"2022 7th International Conference on Computer and Communication Systems (ICCCS)\",\"volume\":\"239 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-04-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 7th International Conference on Computer and Communication Systems (ICCCS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/icccs55155.2022.9846750\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 7th International Conference on Computer and Communication Systems (ICCCS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/icccs55155.2022.9846750","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Research on Structure-Activity Relationship of HIV–1 Protease Based on Support Vector Machine Method
Human immunodeficiency virus type 1 (HIV-1) is a kind of retrovirus and can cause AIDS (Acquired Immune Deficiency Syndrome). HIV-1 protease is a retroviral aspartyl protease which is essential for the life-cycle of HIV. HIV-1 protease inhibitors can decrease the activity of HIV-1, and this can reduce the infections of AIDS. In the study of HIV-1protease structure-activity relationship, we built a data set of 1630 compounds including 830 protease inhibitors and 800 HIV-1 protease decoys. Support Vector Machine (SVM) was used to build the classification model for HIV-1 protease inhibitors and decoys and the quantitative prediction model for HIV-1 protease inhibitors. In the study we use ADRIANA.Code software to calculate the descriptors of HIV-1 protease inhibitors and HIV-1 protease decoys, including 2D and 3D descriptors. The classification model and quantitative prediction model were built for HIV-1 protease inhibitors. The accuracy rates of classification models are over 98%, and Matthews Correlation Coefficient (MCC) of classification models are over 0.96. The linear regression coefficients of the quantitative prediction models are above 0.75